Timer function wrapper and instant timer

analog/analog.py

@@ -17,6 +17,7 @@
 from analog.lora import LoRAHandler
 from analog.lora.utils import is_lora
 from analog.state import AnaLogState
+from analog.timer.timer import DeviceFunctionTimer
 from analog.utils import (
     get_logger,
     get_rank,
@@ -237,6 +238,7 @@ def __exit__(self, exc_type, exc_value, traceback) -> None:
         """
         self.logger.update()
 
+    @DeviceFunctionTimer.timer
     def build_log_dataset(self):
         """
         Constructs the log dataset from the stored logs. This dataset can then be used
@@ -249,6 +251,7 @@ def build_log_dataset(self):
         log_dataset = LogDataset(log_dir=self.log_dir, config=self.influence_config)
         return log_dataset
 
+    @DeviceFunctionTimer.timer
     def build_log_dataloader(
         self, batch_size: int = 16, num_workers: int = 0, pin_memory: bool = False
     ):

analog/analysis/influence_function.py

@@ -5,6 +5,7 @@
 from einops import einsum, rearrange, reduce
 from analog.config import InfluenceConfig
 from analog.state import AnaLogState
+from analog.timer.timer import DeviceFunctionTimer
 from analog.utils import get_logger, nested_dict
 from analog.analysis.utils import synchronize_device
 
@@ -24,6 +25,7 @@ def __init__(self, config: InfluenceConfig, state: AnaLogState):
         self.influence_scores = pd.DataFrame()
         self.flatten = config.flatten
 
+    @DeviceFunctionTimer.timer
     @torch.no_grad()
     def precondition(
         self,
@@ -212,6 +214,7 @@ def flatten_log(self, src):
             to_cat.append(log.view(bsz, -1))
         return torch.cat(to_cat, dim=1)
 
+    @DeviceFunctionTimer.timer
     def compute_influence_all(
         self,
         src_log: Tuple[str, Dict[str, Dict[str, torch.Tensor]]],

analog/logging/log_saver.py

@@ -1,6 +1,7 @@
 from concurrent.futures import ThreadPoolExecutor
 import torch
 
+from analog.timer.timer import HostFunctionTimer, DeviceFunctionTimer
 from analog.utils import nested_dict, to_numpy, get_rank
 from analog.logging.mmap import MemoryMapHandler
 
@@ -21,6 +22,7 @@ def __init__(self, config, state):
         self.buffer = nested_dict()
         self.buffer_size = 0
 
+    @DeviceFunctionTimer.timer
     def buffer_write(self, binfo):
         """
         Add log state on exit.
@@ -85,6 +87,7 @@ def _flush_serialized(self, log_dir) -> str:
         del buffer_list
         return log_dir
 
+    @DeviceFunctionTimer.timer
     def flush(self) -> None:
         """
         For the DefaultHandler, there's no batch operation needed since each add operation writes to the file.

analog/logging/logger.py

@@ -10,6 +10,7 @@
 from analog.logging.option import LogOption
 from analog.logging.log_saver import LogSaver
 from analog.logging.utils import compute_per_sample_gradient
+from analog.timer.timer import DeviceFunctionTimer
 from analog.utils import get_logger
 
 
@@ -42,6 +43,7 @@ def __init__(
         self.grad_hooks = []
         self.tensor_hooks = []
 
+    @DeviceFunctionTimer.timer
     def log(self, data_id: Any, mask: Optional[torch.Tensor] = None):
         """
         Add log state on exit.
@@ -59,6 +61,7 @@ def log(self, data_id: Any, mask: Optional[torch.Tensor] = None):
 
         return log
 
+    @DeviceFunctionTimer.timer
     def update(self):
         # Update statistics
         for stat in self.opt.statistic["grad"]:
@@ -82,6 +85,7 @@ def update(self):
             self.log_saver.buffer_write(binfo=self.binfo)
             self.log_saver.flush()
 
+    @DeviceFunctionTimer.timer
     def _forward_hook_fn(
         self, module: nn.Module, inputs: Tuple[torch.Tensor], module_name: str
     ) -> None:
@@ -131,6 +135,7 @@ def _forward_hook_fn(
                 cpu_offload=self.cpu_offload,
             )
 
+    @DeviceFunctionTimer.timer
     def _backward_hook_fn(
         self,
         module: nn.Module,
@@ -172,6 +177,7 @@ def _backward_hook_fn(
                 cpu_offload=self.cpu_offload,
             )
 
+    @DeviceFunctionTimer.timer
     def _grad_hook_fn(
         self,
         module: nn.Module,
@@ -270,6 +276,7 @@ def _tensor_backward_hook_fn(self, grad: torch.Tensor, tensor_name: str) -> None
                 cpu_offload=self.cpu_offload,
             )
 
+    @DeviceFunctionTimer.timer
     def register_all_module_hooks(self) -> None:
         """
         Register all module hooks.

analog/timer/__init__.py

@@ -0,0 +1 @@
+from .timer import FunctionTimer, Timer

analog/timer/timer.py

@@ -0,0 +1,186 @@
+import logging
+import time
+import functools
+
+import torch
+import psutil
+import os
+
+
+def get_gpu_memory(device_index=None):
+    return torch.cuda.memory_allocated(device_index)
+
+
+def get_gpu_max_memory(device_index=None):
+    return torch.cuda.max_memory_allocated(device_index)
+
+
+def get_host_memory():
+    process = psutil.Process(os.getpid())
+    return process.memory_info().rss
+
+
+def get_cpu_swap_memory():
+    return psutil.swap_memory().used
+
+
+class FunctionTimer:
+    log = {}
+
+    @classmethod
+    def _wrap_function(cls, func, label, host_timer):
+        @functools.wraps(func)
+        def wrapper(*args, **kwargs):
+            if host_timer:
+                return cls._host_timer_wrapper(func, label, *args, **kwargs)
+            else:
+                return cls._device_timer_wrapper(func, label, *args, **kwargs)
+
+        return wrapper
+
+    @classmethod
+    def _host_timer_wrapper(cls, func, label, *args, **kwargs):
+        before_memory = get_host_memory()
+        start_time = time.time()
+        result = func(*args, **kwargs)
+        end_time = time.time()
+        after_memory = get_host_memory()
+        if label not in cls.log:
+            cls.log[label] = [
+                {
+                    "time_delta": end_time - start_time,
+                    "memory_delta": (before_memory - after_memory) >> 20,
+                }
+            ]
+        else:
+            cls.log[label].append(
+                {
+                    "time_delta": end_time - start_time,
+                    "memory_delta": (before_memory - after_memory) >> 20,
+                }
+            )
+        return result
+
+    @classmethod
+    def _device_timer_wrapper(cls, func, label, *args, **kwargs):
+        before_memory = get_gpu_memory()
+        start_event = torch.cuda.Event(enable_timing=True)
+        start_event.record()
+        result = func(*args, **kwargs)
+        end_event = torch.cuda.Event(enable_timing=True)
+        end_event.record()
+        after_memory = get_gpu_memory()
+        torch.cuda.current_stream().wait_event(end_event)
+        torch.cuda.synchronize()
+        if label not in cls.log:
+            cls.log[label] = [
+                {
+                    "time_delta": start_event.elapsed_time(end_event)
+                    / 1000,  # turn to seconds
+                    "memory_delta": (before_memory - after_memory) >> 20,
+                }
+            ]
+        else:
+            cls.log[label].append(
+                {
+                    "time_delta": start_event.elapsed_time(end_event)
+                    / 1000,  # turn to seconds
+                    "memory_delta": (before_memory - after_memory) >> 20,
+                }
+            )
+        return result
+
+    @classmethod
+    def timer(cls, label_or_func=None):
+        host_timer = getattr(
+            cls, "host_timer", False
+        )  # Fallback to False if not defined
+
+        def decorator(func):
+            label = label_or_func if isinstance(label_or_func, str) else func.__name__
+            return cls._wrap_function(func, label, host_timer)
+
+        if callable(label_or_func):
+            return decorator(label_or_func)
+        return decorator
+
+    @classmethod
+    def get_log(cls):
+        return cls.log
+
+    @classmethod
+    def print_log(cls):
+        print("Function Timer Logs:")
+        for label, details in cls.log.items():
+            print(f"  {label}:")
+            sum_time = 0
+            for log in details:
+                for key, value in log.items():
+                    if key == "time_delta":
+                        sum_time += value
+            print(f"    operation costs {sum_time} seconds")
+
+
+class HostFunctionTimer(FunctionTimer):
+    host_timer = True
+
+
+class DeviceFunctionTimer(FunctionTimer):
+    if torch.cuda.is_available():
+        host_timer = False
+    else:
+        logging.warning("CUDA is not set, setting the timer is set to host timer.")
+        host_timer = True
+
+
+class Timer:
+    def __init__(self):
+        self.timers = {
+            "cpu": {},
+            "gpu": {},
+        }
+        self.timer_info = {}  # synchronized.
+        self.is_synchronized = False
+
+    def start_timer(self, name, host_timer=False):
+        if host_timer:
+            if name in self.timers["cpu"]:
+                logging.warning(f"timer for {name} already exist")
+                return
+            start_time = time.time()
+            self.timers["cpu"][name] = [start_time]
+        else:
+            if name in self.timers["gpu"]:
+                logging.warning(f"timer for {name} already exist")
+                return
+            self.is_synchronized = False
+            start_event = torch.cuda.Event(enable_timing=True)
+            start_event.record()
+            self.timers["gpu"][name] = [start_event]
+
+    def stop_timer(self, name):
+        if name in self.timers["cpu"]:
+            end_time = time.time()
+            self.timers["cpu"][name].append(end_time)
+        if name in self.timers["gpu"]:
+            self.is_synchronized = False
+            end_event = torch.cuda.Event(enable_timing=True)
+            end_event.record()
+            self.timers["gpu"][name].append(end_event)
+
+    def _calculate_elapse_time(self):
+        for name, timer in self.timers["cpu"].items():
+            assert len(timer) == 2
+            self.timer_info[name] = (timer[1] - timer[0]) * 1000
+        if not self.is_synchronized:
+            for name, events in self.timers["gpu"].items():
+                assert len(events) == 2
+                torch.cuda.current_stream().wait_event(events[1])
+                torch.cuda.synchronize()
+                self.timer_info[name] = events[0].elapsed_time(events[1])
+            self.is_synchronized = True
+
+    def get_info(self):
+        if not self.is_synchronized:
+            self._calculate_elapse_time()
+        return self.timer_info

examples/mnist_influence/compute_influences.py

@@ -11,6 +11,8 @@
     construct_mlp,
 )
 
+from analog.timer import FunctionTimer
+
 parser = argparse.ArgumentParser("MNIST Influence Analysis")
 parser.add_argument("--data", type=str, default="mnist", help="mnist or fmnist")
 parser.add_argument("--eval-idxs", type=int, nargs="+", default=[0])
@@ -79,6 +81,7 @@
 )
 _, top_influential_data = torch.topk(if_scores, k=10)
 
+FunctionTimer.print_log()
 # Save
 if_scores = if_scores.cpu().numpy().tolist()[0]
 torch.save(if_scores, "if_analog.pt")

requirements.txt

@@ -2,6 +2,5 @@ numpy
 pandas
 torch
 einops
-
+psutil
 pyyaml
-